Response to Letter to Editor: “Best Practices for Chiropractic Care for Older Adults: A Consensus Update”
Bibliographic record
Abstract
We wish to thank the author of the articulate and well-referenced letter to the editor. We certainly agree that there are many other commonly used nonthrust types of manipulation procedures, such as flexion-distraction and instrument-assisted techniques. Nonthrust techniques may, indeed, be viable and alternative options to thrust manipulation procedures. Any decision concerning patient treatment must be informed by evidence, clinician experience, and/or patient preference to achieve the optimal outcome. Response to “Best Practices for Chiropractic Care for Older Adults: A Systematic Review and Consensus Update”Journal of Manipulative & Physiological TherapeuticsVol. 40Issue 7PreviewHawk et al.1 reported a Delphi study on the effectiveness or efficacy of spinal manipulation thearpy (SMT) in persons over age 65 years and the adverse events associated with chiropractic care, including SMT. Complementing this study is a review by Nielsen et al.,2 who identified 118 studies on the adverse and serious adverse events associated with SMT; 54 (46%) reported that it is safe, 15 (13%) reported that it is harmful, and 49 (42%) were neutral or unclear. Nielsen et al. stated that it is currently not possible to provide an overall conclusion about the safety of SMT; however, the types of serious adverse events reported can, indeed, be significant, indicating that some risk is present. Full-Text PDF
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.024 | 0.020 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".